Integrating data-dependent and data-independent acquisition methods for comprehensive lipidomic profiling using ion mobility spectrometry-mass spectrometry and a streamlined data analysis workflow

Abstract

Lipidomics provides unique insights into the roles of lipids in cellular processes. In liquid chromatography tandem mass spectrometry-based lipidomics, data-dependent acquisition (DDA) provides cleaner spectra and supports higher confidence in lipid identification. Data-independent acquisition (DIA) allows more comprehensive characterization, but lipid identification is complicated by the highly multiplexed nature of MS/MS spectra without explicitly isolated precursor ions. In this work, we present an integrated workflow for comprehensive lipidomics analysis that incorporates both DDA (without ion mobility separation) and DIA (with ion mobility separation), leveraging the strengths of both acquisition methods. To streamline the data analysis, we developed a highly modular, Python-based data analysis workflow LipidIMEA . We applied this combined experimental and data analysis workflow to analyze complex lipid extracts from breast cancer patient-derived xenografts, evaluating the resulting lipid annotations for their coverage, confidence and repeatability.